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Tier-1 brand, metro location, mid-level generalist Databricks role, and broad skillset requirements increase competition.
Core data engineering skills are transferable but banking compliance and Databricks specialization raise domain sensitivity.
Mandatory Databricks experience, specific cloud/Databricks stack, regulated finance compliance, and technical leadership make filters strict.
Design, develop, and deploy scalable batch and real-time data pipelines using Python, PySpark, Spark SQL, and Databricks.
Manage and optimize Databricks cloud infrastructure on AWS or GCP including secure integrations and performance tuning using Delta Lake features.
Lead technical best practices, mentoring, automated CI/CD pipeline establishment, and enforce data governance compliance including financial regulations.
Strong proficiency in Python (including pandas, pytest) and advanced SQL (window functions, CTEs, query optimization).
Deep hands-on experience with Apache Spark (PySpark) and at least 3 years developing on Databricks, including Delta Lake ACID transactions and Unity Catalog.
Experience deploying and managing Databricks environments on AWS or GCP with cloud-native components like S3/GCS, IAM, and related services.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced technical lead able to guide senior and mid-level engineers within Agile/Scrum environments.
Expert in cloud-native data engineering with strong focus on Databricks lakehouse platform and distributed data processing.
Skilled at collaborating cross-functionally with Data Science and BI teams to operationalize ML models and build semantic layers.